Overview

Dataset statistics

Number of variables22
Number of observations2938
Missing cells2563
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory505.1 KiB
Average record size in memory176.0 B

Variable types

Categorical2
Numeric20

Alerts

Country has a high cardinality: 193 distinct values High cardinality
Life expectancy is highly correlated with Status and 12 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancy and 6 other fieldsHigh correlation
infant deaths is highly correlated with Measles and 4 other fieldsHigh correlation
Alcohol is highly correlated with Status and 5 other fieldsHigh correlation
percentage expenditure is highly correlated with Status and 3 other fieldsHigh correlation
Hepatitis B is highly correlated with Polio and 1 other fieldsHigh correlation
Measles is highly correlated with infant deaths and 1 other fieldsHigh correlation
BMI is highly correlated with Status and 9 other fieldsHigh correlation
under-five deaths is highly correlated with infant deaths and 4 other fieldsHigh correlation
Polio is highly correlated with Life expectancy and 5 other fieldsHigh correlation
Diphtheria is highly correlated with Life expectancy and 6 other fieldsHigh correlation
HIV/AIDS is highly correlated with Life expectancy and 1 other fieldsHigh correlation
GDP is highly correlated with percentage expenditure and 1 other fieldsHigh correlation
thinness 1-19 years is highly correlated with Status and 9 other fieldsHigh correlation
thinness 5-9 years is highly correlated with Life expectancy and 6 other fieldsHigh correlation
Income composition of resources is highly correlated with Status and 10 other fieldsHigh correlation
Schooling is highly correlated with Status and 11 other fieldsHigh correlation
Population is highly correlated with infant deaths and 2 other fieldsHigh correlation
Status is highly correlated with Life expectancy and 7 other fieldsHigh correlation
Total expenditure is highly correlated with Status and 5 other fieldsHigh correlation
Alcohol has 194 (6.6%) missing values Missing
Hepatitis B has 553 (18.8%) missing values Missing
BMI has 34 (1.2%) missing values Missing
Total expenditure has 226 (7.7%) missing values Missing
GDP has 448 (15.2%) missing values Missing
Population has 652 (22.2%) missing values Missing
thinness 1-19 years has 34 (1.2%) missing values Missing
thinness 5-9 years has 34 (1.2%) missing values Missing
Income composition of resources has 167 (5.7%) missing values Missing
Schooling has 163 (5.5%) missing values Missing
infant deaths has 848 (28.9%) zeros Zeros
percentage expenditure has 611 (20.8%) zeros Zeros
Measles has 983 (33.5%) zeros Zeros
under-five deaths has 785 (26.7%) zeros Zeros
Income composition of resources has 130 (4.4%) zeros Zeros

Reproduction

Analysis started2022-10-03 16:40:06.664063
Analysis finished2022-10-03 16:41:04.421758
Duration57.76 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Afghanistan
 
16
Peru
 
16
Nicaragua
 
16
Niger
 
16
Nigeria
 
16
Other values (188)
2858 

Length

Max length52
Median length34
Mean length10.04118448
Min length4

Characters and Unicode

Total characters29501
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.3%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan16
 
0.5%
Peru16
 
0.5%
Nicaragua16
 
0.5%
Niger16
 
0.5%
Nigeria16
 
0.5%
Norway16
 
0.5%
Oman16
 
0.5%
Pakistan16
 
0.5%
Panama16
 
0.5%
Papua New Guinea16
 
0.5%
Other values (183)2778
94.6%

Length

2022-10-03T16:41:04.514064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic192
 
4.5%
of192
 
4.5%
and97
 
2.3%
united64
 
1.5%
democratic48
 
1.1%
the48
 
1.1%
guinea48
 
1.1%
saint33
 
0.8%
ireland32
 
0.7%
congo32
 
0.7%
Other values (223)3502
81.7%

Most occurring characters

ValueCountFrequency (%)
a4190
 
14.2%
i2535
 
8.6%
e2178
 
7.4%
n2104
 
7.1%
o1638
 
5.6%
r1635
 
5.5%
1350
 
4.6%
u1126
 
3.8%
l1110
 
3.8%
t1107
 
3.8%
Other values (46)10528
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23976
81.3%
Uppercase Letter3967
 
13.4%
Space Separator1350
 
4.6%
Open Punctuation64
 
0.2%
Close Punctuation64
 
0.2%
Other Punctuation48
 
0.2%
Dash Punctuation32
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4190
17.5%
i2535
10.6%
e2178
 
9.1%
n2104
 
8.8%
o1638
 
6.8%
r1635
 
6.8%
u1126
 
4.7%
l1110
 
4.6%
t1107
 
4.6%
d867
 
3.6%
Other values (17)5486
22.9%
Uppercase Letter
ValueCountFrequency (%)
S466
 
11.7%
B336
 
8.5%
C289
 
7.3%
M275
 
6.9%
A256
 
6.5%
G240
 
6.0%
R240
 
6.0%
T209
 
5.3%
I194
 
4.9%
P193
 
4.9%
Other values (14)1269
32.0%
Space Separator
ValueCountFrequency (%)
1350
100.0%
Open Punctuation
ValueCountFrequency (%)
(64
100.0%
Close Punctuation
ValueCountFrequency (%)
)64
100.0%
Other Punctuation
ValueCountFrequency (%)
'48
100.0%
Dash Punctuation
ValueCountFrequency (%)
-32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27943
94.7%
Common1558
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4190
15.0%
i2535
 
9.1%
e2178
 
7.8%
n2104
 
7.5%
o1638
 
5.9%
r1635
 
5.9%
u1126
 
4.0%
l1110
 
4.0%
t1107
 
4.0%
d867
 
3.1%
Other values (41)9453
33.8%
Common
ValueCountFrequency (%)
1350
86.6%
(64
 
4.1%
)64
 
4.1%
'48
 
3.1%
-32
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII29485
99.9%
None16
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4190
 
14.2%
i2535
 
8.6%
e2178
 
7.4%
n2104
 
7.1%
o1638
 
5.6%
r1635
 
5.5%
1350
 
4.6%
u1126
 
3.8%
l1110
 
3.8%
t1107
 
3.8%
Other values (45)10512
35.7%
None
ValueCountFrequency (%)
ô16
100.0%

Year
Real number (ℝ≥0)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.51872
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:04.628111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.61384094
Coefficient of variation (CV)0.002298280406
Kurtosis-1.213721712
Mean2007.51872
Median Absolute Deviation (MAD)4
Skewness-0.006409027359
Sum5898090
Variance21.28752822
MonotonicityNot monotonic
2022-10-03T16:41:04.735659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013193
 
6.6%
2015183
 
6.2%
2014183
 
6.2%
2012183
 
6.2%
2011183
 
6.2%
2010183
 
6.2%
2009183
 
6.2%
2008183
 
6.2%
2007183
 
6.2%
2006183
 
6.2%
Other values (6)1098
37.4%
ValueCountFrequency (%)
2000183
6.2%
2001183
6.2%
2002183
6.2%
2003183
6.2%
2004183
6.2%
2005183
6.2%
2006183
6.2%
2007183
6.2%
2008183
6.2%
2009183
6.2%
ValueCountFrequency (%)
2015183
6.2%
2014183
6.2%
2013193
6.6%
2012183
6.2%
2011183
6.2%
2010183
6.2%
2009183
6.2%
2008183
6.2%
2007183
6.2%
2006183
6.2%

Status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Developing
2426 
Developed
512 

Length

Max length10
Median length10
Mean length9.82573179
Min length9

Characters and Unicode

Total characters28868
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing2426
82.6%
Developed512
 
17.4%

Length

2022-10-03T16:41:04.850562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-03T16:41:05.000414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
developing2426
82.6%
developed512
 
17.4%

Most occurring characters

ValueCountFrequency (%)
e6388
22.1%
D2938
10.2%
v2938
10.2%
l2938
10.2%
o2938
10.2%
p2938
10.2%
i2426
 
8.4%
n2426
 
8.4%
g2426
 
8.4%
d512
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25930
89.8%
Uppercase Letter2938
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6388
24.6%
v2938
11.3%
l2938
11.3%
o2938
11.3%
p2938
11.3%
i2426
 
9.4%
n2426
 
9.4%
g2426
 
9.4%
d512
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D2938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6388
22.1%
D2938
10.2%
v2938
10.2%
l2938
10.2%
o2938
10.2%
p2938
10.2%
i2426
 
8.4%
n2426
 
8.4%
g2426
 
8.4%
d512
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII28868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6388
22.1%
D2938
10.2%
v2938
10.2%
l2938
10.2%
o2938
10.2%
p2938
10.2%
i2426
 
8.4%
n2426
 
8.4%
g2426
 
8.4%
d512
 
1.8%

Life expectancy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct362
Distinct (%)12.4%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.22493169
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:05.104063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163.1
median72.1
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.523867488
Coefficient of variation (CV)0.1375785754
Kurtosis-0.2344773942
Mean69.22493169
Median Absolute Deviation (MAD)5.8
Skewness-0.6386047359
Sum202690.6
Variance90.70405193
MonotonicityNot monotonic
2022-10-03T16:41:05.240023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7345
 
1.5%
7533
 
1.1%
7831
 
1.1%
73.628
 
1.0%
73.925
 
0.9%
7625
 
0.9%
8125
 
0.9%
74.524
 
0.8%
74.724
 
0.8%
73.523
 
0.8%
Other values (352)2645
90.0%
ValueCountFrequency (%)
36.31
< 0.1%
391
< 0.1%
411
< 0.1%
41.51
< 0.1%
42.31
< 0.1%
43.11
< 0.1%
43.31
< 0.1%
43.51
< 0.1%
43.81
< 0.1%
441
< 0.1%
ValueCountFrequency (%)
8911
0.4%
8810
0.3%
879
0.3%
8615
0.5%
8512
0.4%
8411
0.4%
83.71
 
< 0.1%
83.52
 
0.1%
83.41
 
< 0.1%
83.31
 
< 0.1%

Adult Mortality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct425
Distinct (%)14.5%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean164.7964481
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:05.375156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile398.3
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.292079
Coefficient of variation (CV)0.754215764
Kurtosis1.748860208
Mean164.7964481
Median Absolute Deviation (MAD)76
Skewness1.174369488
Sum482524
Variance15448.5209
MonotonicityNot monotonic
2022-10-03T16:41:05.503225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1234
 
1.2%
1430
 
1.0%
1629
 
1.0%
1125
 
0.9%
13825
 
0.9%
1923
 
0.8%
14422
 
0.7%
1521
 
0.7%
1721
 
0.7%
1321
 
0.7%
Other values (415)2677
91.1%
ValueCountFrequency (%)
112
0.4%
28
 
0.3%
36
 
0.2%
44
 
0.1%
52
 
0.1%
613
0.4%
716
0.5%
813
0.4%
912
0.4%
1125
0.9%
ValueCountFrequency (%)
7231
< 0.1%
7171
< 0.1%
7151
< 0.1%
6991
< 0.1%
6931
< 0.1%
6861
< 0.1%
6821
< 0.1%
6791
< 0.1%
6751
< 0.1%
6661
< 0.1%

infant deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.30394826
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:05.650322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265013
Coefficient of variation (CV)3.891456661
Kurtosis116.0427561
Mean30.30394826
Median Absolute Deviation (MAD)3
Skewness9.78696295
Sum89033
Variance13906.65971
MonotonicityNot monotonic
2022-10-03T16:41:05.785300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0848
28.9%
1342
 
11.6%
2203
 
6.9%
3175
 
6.0%
496
 
3.3%
857
 
1.9%
753
 
1.8%
948
 
1.6%
1048
 
1.6%
646
 
1.6%
Other values (199)1022
34.8%
ValueCountFrequency (%)
0848
28.9%
1342
11.6%
2203
 
6.9%
3175
 
6.0%
496
 
3.3%
544
 
1.5%
646
 
1.6%
753
 
1.8%
857
 
1.9%
948
 
1.6%
ValueCountFrequency (%)
18002
0.1%
17002
0.1%
16001
< 0.1%
15002
0.1%
14001
< 0.1%
13002
0.1%
12001
< 0.1%
11002
0.1%
10001
< 0.1%
9571
< 0.1%

Alcohol
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1076
Distinct (%)39.2%
Missing194
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.602860787
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:05.938073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8775
median3.755
Q37.7025
95-th percentile11.96
Maximum17.87
Range17.86
Interquartile range (IQR)6.825

Descriptive statistics

Standard deviation4.052412659
Coefficient of variation (CV)0.8804117366
Kurtosis-0.8029092244
Mean4.602860787
Median Absolute Deviation (MAD)3.245
Skewness0.5895625281
Sum12630.25
Variance16.42204836
MonotonicityNot monotonic
2022-10-03T16:41:06.086426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01288
 
9.8%
0.0315
 
0.5%
0.0413
 
0.4%
0.0212
 
0.4%
0.0912
 
0.4%
0.2110
 
0.3%
0.0610
 
0.3%
1.1810
 
0.3%
0.059
 
0.3%
0.499
 
0.3%
Other values (1066)2356
80.2%
(Missing)194
 
6.6%
ValueCountFrequency (%)
0.01288
9.8%
0.0212
 
0.4%
0.0315
 
0.5%
0.0413
 
0.4%
0.059
 
0.3%
0.0610
 
0.3%
0.074
 
0.1%
0.089
 
0.3%
0.0912
 
0.4%
0.17
 
0.2%
ValueCountFrequency (%)
17.871
< 0.1%
17.311
< 0.1%
16.991
< 0.1%
16.581
< 0.1%
16.351
< 0.1%
15.521
< 0.1%
15.191
< 0.1%
15.141
< 0.1%
15.071
< 0.1%
15.042
0.1%

percentage expenditure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2512955
Minimum0
Maximum19479.91161
Zeros611
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:06.249279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.685342585
median64.91290604
Q3441.5341444
95-th percentile4506.638496
Maximum19479.91161
Range19479.91161
Interquartile range (IQR)436.8488018

Descriptive statistics

Standard deviation1987.914858
Coefficient of variation (CV)2.692734669
Kurtosis26.57338739
Mean738.2512955
Median Absolute Deviation (MAD)64.91290604
Skewness4.652051348
Sum2168982.306
Variance3951805.483
MonotonicityNot monotonic
2022-10-03T16:41:06.610747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0611
 
20.8%
71.279623621
 
< 0.1%
3.3040398991
 
< 0.1%
218.57161791
 
< 0.1%
36.816211751
 
< 0.1%
2.5424369081
 
< 0.1%
2.0923438931
 
< 0.1%
22.355954481
 
< 0.1%
15.255188161
 
< 0.1%
31.502432371
 
< 0.1%
Other values (2318)2318
78.9%
ValueCountFrequency (%)
0611
20.8%
0.099872191
 
< 0.1%
0.1080559731
 
< 0.1%
0.275648261
 
< 0.1%
0.3284180561
 
< 0.1%
0.3586514211
 
< 0.1%
0.3882537721
 
< 0.1%
0.3972287641
 
< 0.1%
0.4428024041
 
< 0.1%
0.53057281
 
< 0.1%
ValueCountFrequency (%)
19479.911611
< 0.1%
19099.045061
< 0.1%
18961.34861
< 0.1%
18822.867321
< 0.1%
18379.329741
< 0.1%
17028.527981
< 0.1%
16255.161981
< 0.1%
15515.752341
< 0.1%
15345.49071
< 0.1%
15268.064451
< 0.1%

Hepatitis B
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct87
Distinct (%)3.6%
Missing553
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean80.94046122
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:06.761167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.07001559
Coefficient of variation (CV)0.3097340343
Kurtosis2.770259399
Mean80.94046122
Median Absolute Deviation (MAD)6
Skewness-1.930845104
Sum193043
Variance628.5056818
MonotonicityNot monotonic
2022-10-03T16:41:06.903585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99240
 
8.2%
98210
 
7.1%
96167
 
5.7%
97155
 
5.3%
95149
 
5.1%
94127
 
4.3%
93101
 
3.4%
9292
 
3.1%
9175
 
2.6%
8971
 
2.4%
Other values (77)998
34.0%
(Missing)553
18.8%
ValueCountFrequency (%)
11
 
< 0.1%
24
 
0.1%
44
 
0.1%
59
 
0.3%
617
 
0.6%
720
 
0.7%
839
1.3%
965
2.2%
111
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
99240
8.2%
98210
7.1%
97155
5.3%
96167
5.7%
95149
5.1%
94127
4.3%
93101
3.4%
9292
 
3.1%
9175
 
2.6%
8971
 
2.4%

Measles
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.59224
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:07.048208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.27249
Coefficient of variation (CV)4.739340911
Kurtosis114.8599032
Mean2419.59224
Median Absolute Deviation (MAD)17
Skewness9.441331947
Sum7108762
Variance131498338.3
MonotonicityNot monotonic
2022-10-03T16:41:07.182328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0983
33.5%
1104
 
3.5%
268
 
2.3%
344
 
1.5%
433
 
1.1%
629
 
1.0%
728
 
1.0%
525
 
0.9%
824
 
0.8%
922
 
0.7%
Other values (948)1578
53.7%
ValueCountFrequency (%)
0983
33.5%
1104
 
3.5%
268
 
2.3%
344
 
1.5%
433
 
1.1%
525
 
0.9%
629
 
1.0%
728
 
1.0%
824
 
0.8%
922
 
0.7%
ValueCountFrequency (%)
2121831
< 0.1%
1824851
< 0.1%
1681071
< 0.1%
1412581
< 0.1%
1338021
< 0.1%
1314411
< 0.1%
1242191
< 0.1%
1187121
< 0.1%
1109271
< 0.1%
1090231
< 0.1%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct608
Distinct (%)20.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.32124656
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:07.322874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.3
median43.5
Q356.2
95-th percentile64.785
Maximum87.3
Range86.3
Interquartile range (IQR)36.9

Descriptive statistics

Standard deviation20.0440335
Coefficient of variation (CV)0.5230527528
Kurtosis-1.291095468
Mean38.32124656
Median Absolute Deviation (MAD)16.3
Skewness-0.2193116034
Sum111284.9
Variance401.7632791
MonotonicityNot monotonic
2022-10-03T16:41:07.457445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.518
 
0.6%
55.816
 
0.5%
5716
 
0.5%
54.215
 
0.5%
59.915
 
0.5%
59.314
 
0.5%
52.813
 
0.4%
5513
 
0.4%
59.413
 
0.4%
56.513
 
0.4%
Other values (598)2758
93.9%
(Missing)34
 
1.2%
ValueCountFrequency (%)
11
 
< 0.1%
1.42
 
0.1%
1.81
 
< 0.1%
1.91
 
< 0.1%
21
 
< 0.1%
2.111
0.4%
2.29
0.3%
2.36
0.2%
2.45
0.2%
2.58
0.3%
ValueCountFrequency (%)
87.31
< 0.1%
83.31
< 0.1%
82.81
< 0.1%
81.61
< 0.1%
79.31
< 0.1%
77.61
< 0.1%
77.31
< 0.1%
77.11
< 0.1%
76.71
< 0.1%
76.21
< 0.1%

under-five deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.0357386
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:07.597103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.4455484
Coefficient of variation (CV)3.816884246
Kurtosis109.7527951
Mean42.0357386
Median Absolute Deviation (MAD)4
Skewness9.495064657
Sum123501
Variance25742.774
MonotonicityNot monotonic
2022-10-03T16:41:07.746852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0785
26.7%
1361
 
12.3%
2163
 
5.5%
4161
 
5.5%
3129
 
4.4%
1253
 
1.8%
849
 
1.7%
648
 
1.6%
1047
 
1.6%
544
 
1.5%
Other values (242)1098
37.4%
ValueCountFrequency (%)
0785
26.7%
1361
12.3%
2163
 
5.5%
3129
 
4.4%
4161
 
5.5%
544
 
1.5%
648
 
1.6%
730
 
1.0%
849
 
1.7%
940
 
1.4%
ValueCountFrequency (%)
25001
< 0.1%
24001
< 0.1%
23001
< 0.1%
22001
< 0.1%
21001
< 0.1%
20002
0.1%
19001
< 0.1%
18001
< 0.1%
17001
< 0.1%
16001
< 0.1%

Polio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct73
Distinct (%)2.5%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.55018842
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:07.890250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.42804595
Coefficient of variation (CV)0.2838036641
Kurtosis3.776509819
Mean82.55018842
Median Absolute Deviation (MAD)6
Skewness-2.098053249
Sum240964
Variance548.873337
MonotonicityNot monotonic
2022-10-03T16:41:08.027569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99376
 
12.8%
98255
 
8.7%
96207
 
7.0%
97205
 
7.0%
95180
 
6.1%
94159
 
5.4%
93120
 
4.1%
9296
 
3.3%
9188
 
3.0%
971
 
2.4%
Other values (63)1162
39.6%
ValueCountFrequency (%)
37
 
0.2%
411
 
0.4%
58
 
0.3%
611
 
0.4%
724
 
0.8%
840
1.4%
971
2.4%
171
 
< 0.1%
231
 
< 0.1%
242
 
0.1%
ValueCountFrequency (%)
99376
12.8%
98255
8.7%
97205
7.0%
96207
7.0%
95180
6.1%
94159
5.4%
93120
 
4.1%
9296
 
3.3%
9188
 
3.0%
8956
 
1.9%

Total expenditure
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct818
Distinct (%)30.2%
Missing226
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.938189528
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:08.164947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.26
median5.755
Q37.4925
95-th percentile9.76
Maximum17.6
Range17.23
Interquartile range (IQR)3.2325

Descriptive statistics

Standard deviation2.498319672
Coefficient of variation (CV)0.4207207703
Kurtosis1.156270469
Mean5.938189528
Median Absolute Deviation (MAD)1.59
Skewness0.6186855521
Sum16104.37
Variance6.241601184
MonotonicityNot monotonic
2022-10-03T16:41:08.305875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.615
 
0.5%
6.712
 
0.4%
5.611
 
0.4%
9.110
 
0.3%
5.6410
 
0.3%
5.910
 
0.3%
5.310
 
0.3%
5.2510
 
0.3%
3.410
 
0.3%
4.29
 
0.3%
Other values (808)2605
88.7%
(Missing)226
 
7.7%
ValueCountFrequency (%)
0.371
 
< 0.1%
0.651
 
< 0.1%
0.741
 
< 0.1%
0.761
 
< 0.1%
0.921
 
< 0.1%
1.12
0.1%
1.123
0.1%
1.152
0.1%
1.172
0.1%
1.183
0.1%
ValueCountFrequency (%)
17.61
< 0.1%
17.241
< 0.1%
17.22
0.1%
17.141
< 0.1%
171
< 0.1%
16.91
< 0.1%
16.611
< 0.1%
16.21
< 0.1%
15.61
< 0.1%
15.571
< 0.1%

Diphtheria
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)2.8%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.32408359
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:08.453459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.71691207
Coefficient of variation (CV)0.2880920265
Kurtosis3.558143
Mean82.32408359
Median Absolute Deviation (MAD)6
Skewness-2.072752929
Sum240304
Variance562.4919181
MonotonicityNot monotonic
2022-10-03T16:41:08.590891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99350
 
11.9%
98254
 
8.6%
97205
 
7.0%
96201
 
6.8%
95200
 
6.8%
94149
 
5.1%
93120
 
4.1%
92100
 
3.4%
9191
 
3.1%
8976
 
2.6%
Other values (71)1173
39.9%
ValueCountFrequency (%)
21
 
< 0.1%
34
 
0.1%
412
 
0.4%
510
 
0.3%
616
 
0.5%
721
 
0.7%
839
1.3%
967
2.3%
161
 
< 0.1%
191
 
< 0.1%
ValueCountFrequency (%)
99350
11.9%
98254
8.6%
97205
7.0%
96201
6.8%
95200
6.8%
94149
5.1%
93120
 
4.1%
92100
 
3.4%
9191
 
3.1%
8976
 
2.6%

HIV/AIDS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.742103472
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:08.730494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.077784531
Coefficient of variation (CV)2.914743363
Kurtosis34.89200787
Mean1.742103472
Median Absolute Deviation (MAD)0
Skewness5.396112042
Sum5118.3
Variance25.78389574
MonotonicityNot monotonic
2022-10-03T16:41:08.874883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11781
60.6%
0.2124
 
4.2%
0.3115
 
3.9%
0.469
 
2.3%
0.542
 
1.4%
0.635
 
1.2%
0.932
 
1.1%
0.832
 
1.1%
0.729
 
1.0%
1.521
 
0.7%
Other values (190)658
 
22.4%
ValueCountFrequency (%)
0.11781
60.6%
0.2124
 
4.2%
0.3115
 
3.9%
0.469
 
2.3%
0.542
 
1.4%
0.635
 
1.2%
0.729
 
1.0%
0.832
 
1.1%
0.932
 
1.1%
112
 
0.4%
ValueCountFrequency (%)
50.61
< 0.1%
50.31
< 0.1%
49.91
< 0.1%
49.11
< 0.1%
48.81
< 0.1%
46.41
< 0.1%
43.71
< 0.1%
43.51
< 0.1%
42.11
< 0.1%
40.71
< 0.1%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2490
Distinct (%)100.0%
Missing448
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean7483.158469
Minimum1.68135
Maximum119172.7418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:09.013728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile68.05001537
Q1463.935626
median1766.947595
Q35910.806335
95-th percentile41606.84833
Maximum119172.7418
Range119171.0605
Interquartile range (IQR)5446.870709

Descriptive statistics

Standard deviation14270.16934
Coefficient of variation (CV)1.906971421
Kurtosis12.33307364
Mean7483.158469
Median Absolute Deviation (MAD)1592.456071
Skewness3.20665487
Sum18633064.59
Variance203637733
MonotonicityNot monotonic
2022-10-03T16:41:09.396328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
584.259211
 
< 0.1%
354.81859981
 
< 0.1%
358.997311
 
< 0.1%
43.6464981
 
< 0.1%
416.148381
 
< 0.1%
391.5155241
 
< 0.1%
375.58198661
 
< 0.1%
348.1515111
 
< 0.1%
341.28946181
 
< 0.1%
292.559621
 
< 0.1%
Other values (2480)2480
84.4%
(Missing)448
 
15.2%
ValueCountFrequency (%)
1.681351
< 0.1%
3.6859491
< 0.1%
4.61357451
< 0.1%
5.66872641
< 0.1%
8.3764321
< 0.1%
11.1472771
< 0.1%
11.336781
< 0.1%
11.5531961
< 0.1%
11.6313771
< 0.1%
12.17892791
< 0.1%
ValueCountFrequency (%)
119172.74181
< 0.1%
115761.5771
< 0.1%
114293.84331
< 0.1%
113751.851
< 0.1%
89739.71171
< 0.1%
88564.822981
< 0.1%
87998.444681
< 0.1%
87646.753461
< 0.1%
86852.71191
< 0.1%
85948.7461
< 0.1%

Population
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2278
Distinct (%)99.7%
Missing652
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean12753375.12
Minimum34
Maximum1293859294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:09.537796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile9617.5
Q1195793.25
median1386542
Q37420359
95-th percentile47554415.75
Maximum1293859294
Range1293859260
Interquartile range (IQR)7224565.75

Descriptive statistics

Standard deviation61012096.51
Coefficient of variation (CV)4.783996074
Kurtosis298.0102666
Mean12753375.12
Median Absolute Deviation (MAD)1357309.5
Skewness15.9162356
Sum2.915421552 × 1010
Variance3.72247592 × 1015
MonotonicityNot monotonic
2022-10-03T16:41:09.684248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4444
 
0.1%
7182392
 
0.1%
11412
 
0.1%
268682
 
0.1%
1274452
 
0.1%
2922
 
0.1%
514481961
 
< 0.1%
122621
 
< 0.1%
152285251
 
< 0.1%
146683381
 
< 0.1%
Other values (2268)2268
77.2%
(Missing)652
 
22.2%
ValueCountFrequency (%)
341
< 0.1%
361
< 0.1%
411
< 0.1%
431
< 0.1%
1231
< 0.1%
1351
< 0.1%
1461
< 0.1%
2861
< 0.1%
2922
0.1%
2971
< 0.1%
ValueCountFrequency (%)
12938592941
< 0.1%
11796812391
< 0.1%
11619777191
< 0.1%
11441186741
< 0.1%
11261357771
< 0.1%
2581621131
< 0.1%
2551311161
< 0.1%
2488832321
< 0.1%
2425241231
< 0.1%
2361592761
< 0.1%

thinness 1-19 years
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct200
Distinct (%)6.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.839703857
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:09.827476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.2
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.420194947
Coefficient of variation (CV)0.9133193018
Kurtosis3.97043867
Mean4.839703857
Median Absolute Deviation (MAD)2.3
Skewness1.711471088
Sum14054.5
Variance19.53812337
MonotonicityNot monotonic
2022-10-03T16:41:09.955983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174
 
2.5%
1.965
 
2.2%
0.864
 
2.2%
0.763
 
2.1%
1.262
 
2.1%
2.161
 
2.1%
1.560
 
2.0%
2.258
 
2.0%
0.957
 
1.9%
257
 
1.9%
Other values (190)2283
77.7%
ValueCountFrequency (%)
0.128
 
1.0%
0.240
1.4%
0.332
1.1%
0.45
 
0.2%
0.535
1.2%
0.641
1.4%
0.763
2.1%
0.864
2.2%
0.957
1.9%
174
2.5%
ValueCountFrequency (%)
27.71
 
< 0.1%
27.51
 
< 0.1%
27.41
 
< 0.1%
27.31
 
< 0.1%
27.22
0.1%
27.12
0.1%
273
0.1%
26.92
0.1%
26.82
0.1%
26.71
 
< 0.1%

thinness 5-9 years
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct207
Distinct (%)7.1%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.870316804
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:10.096770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.3
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.508882087
Coefficient of variation (CV)0.9257882532
Kurtosis4.358730342
Mean4.870316804
Median Absolute Deviation (MAD)2.3
Skewness1.777423977
Sum14143.4
Variance20.33001767
MonotonicityNot monotonic
2022-10-03T16:41:10.229762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.969
 
2.3%
1.167
 
2.3%
0.563
 
2.1%
1.963
 
2.1%
162
 
2.1%
2.161
 
2.1%
1.359
 
2.0%
1.557
 
1.9%
1.755
 
1.9%
0.654
 
1.8%
Other values (197)2294
78.1%
ValueCountFrequency (%)
0.137
1.3%
0.245
1.5%
0.325
 
0.9%
0.417
 
0.6%
0.563
2.1%
0.654
1.8%
0.746
1.6%
0.836
1.2%
0.969
2.3%
162
2.1%
ValueCountFrequency (%)
28.61
< 0.1%
28.51
< 0.1%
28.41
< 0.1%
28.31
< 0.1%
28.21
< 0.1%
28.11
< 0.1%
282
0.1%
27.91
< 0.1%
27.82
0.1%
27.71
< 0.1%

Income composition of resources
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct625
Distinct (%)22.6%
Missing167
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.6275510646
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:10.382666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.277
Q10.493
median0.677
Q30.779
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.286

Descriptive statistics

Standard deviation0.2109035552
Coefficient of variation (CV)0.3360739341
Kurtosis1.392814239
Mean0.6275510646
Median Absolute Deviation (MAD)0.127
Skewness-1.14376272
Sum1738.944
Variance0.04448030958
MonotonicityNot monotonic
2022-10-03T16:41:10.520559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130
 
4.4%
0.717
 
0.6%
0.73913
 
0.4%
0.71412
 
0.4%
0.63612
 
0.4%
0.73711
 
0.4%
0.73411
 
0.4%
0.79711
 
0.4%
0.8611
 
0.4%
0.70311
 
0.4%
Other values (615)2532
86.2%
(Missing)167
 
5.7%
ValueCountFrequency (%)
0130
4.4%
0.2531
 
< 0.1%
0.2551
 
< 0.1%
0.2611
 
< 0.1%
0.2661
 
< 0.1%
0.2683
 
0.1%
0.271
 
< 0.1%
0.2761
 
< 0.1%
0.2781
 
< 0.1%
0.2791
 
< 0.1%
ValueCountFrequency (%)
0.9481
 
< 0.1%
0.9451
 
< 0.1%
0.9421
 
< 0.1%
0.9411
 
< 0.1%
0.9391
 
< 0.1%
0.9381
 
< 0.1%
0.9371
 
< 0.1%
0.9365
0.2%
0.9342
 
0.1%
0.9331
 
< 0.1%

Schooling
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct173
Distinct (%)6.2%
Missing163
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean11.99279279
Minimum0
Maximum20.7
Zeros28
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2022-10-03T16:41:10.662018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q110.1
median12.3
Q314.3
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.358919721
Coefficient of variation (CV)0.2800781919
Kurtosis0.8861512689
Mean11.99279279
Median Absolute Deviation (MAD)2.1
Skewness-0.6024365419
Sum33280
Variance11.28234169
MonotonicityNot monotonic
2022-10-03T16:41:10.799424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.958
 
2.0%
13.352
 
1.8%
12.549
 
1.7%
12.846
 
1.6%
12.344
 
1.5%
12.643
 
1.5%
12.442
 
1.4%
10.741
 
1.4%
11.941
 
1.4%
12.740
 
1.4%
Other values (163)2319
78.9%
(Missing)163
 
5.5%
ValueCountFrequency (%)
028
1.0%
2.81
 
< 0.1%
2.94
 
0.1%
31
 
< 0.1%
3.11
 
< 0.1%
3.31
 
< 0.1%
3.41
 
< 0.1%
3.53
 
0.1%
3.61
 
< 0.1%
3.72
 
0.1%
ValueCountFrequency (%)
20.71
 
< 0.1%
20.61
 
< 0.1%
20.51
 
< 0.1%
20.43
0.1%
20.34
0.1%
20.12
0.1%
19.81
 
< 0.1%
19.71
 
< 0.1%
19.53
0.1%
19.32
0.1%

Interactions

2022-10-03T16:41:00.796035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:13.492017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.157980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:18.785016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.522946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.011258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.578703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.246066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.543917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.036036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.469595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:38.746003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.227706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:43.628470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.110207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.588889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:50.874813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.436751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:55.965350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.285390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:00.913076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:13.708004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.287978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:18.912109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.654047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.135480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.705037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.365657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.659492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.150339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.587512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:38.863324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.352493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:43.745742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.228243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.708633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:50.994593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.555343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:56.085527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.409639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:01.022423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:13.832825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.408825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:19.030837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.777618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.253104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.827127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.483724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.768323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.265255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.699099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:38.972947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.471140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:43.857857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.338372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.822090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:51.109023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.666423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:56.199347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.523883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:01.135942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:13.963965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.544630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:19.157179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.913426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.376652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.953886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.603703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.882854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.385975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.815718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:39.088093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.597546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:44.214051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.452907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.940885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:51.228781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.783636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:56.318628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.640858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:01.250135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:14.095680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.667237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:19.284595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:22.043894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.497617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:27.081448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.723885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.997955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.499608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.931666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:39.204844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.720616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:44.329510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.568940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:49.059322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:51.348992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.901693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:56.435978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.758990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:01.366151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:14.232288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.790304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:19.413582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:22.225481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.621031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:27.207929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.846651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:32.118314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.614312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:37.047828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:39.322061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.843324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:44.444029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.686509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:49.178782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:51.469834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:54.020134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:56.554280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.878131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:01.486015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:14.380317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:17.156209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:19.547490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:22.369132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:24.748081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:27.337759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.970797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:32.238349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:34.731028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:37.169193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:39.442980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.971866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:44.563494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.808824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:49.304134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:51.827118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:54.142315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-03T16:41:00.348739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:02.697019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:15.793236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:18.444799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.129028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:23.665059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.239397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:28.888305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.218269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:33.709630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:35.913299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:38.415840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:40.892422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:43.287418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:45.779377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.262803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:50.541904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.090703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:55.629035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:57.939418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:00.461460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:02.810131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:15.918095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:18.562145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.275052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:23.782319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.355388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.011969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.328651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:33.826101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.027174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:38.528647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.010046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:43.405883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:45.894028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.375642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:50.657278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.211338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:55.745361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.055100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:00.577294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:02.919643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:16.038873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:18.675904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:21.404287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:23.899355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:26.467922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:29.133059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:31.438231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:33.933373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:36.136009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:38.639548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:41.121079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:43.518905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:46.001454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:48.483909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:50.768642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:53.326960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:55.857904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:40:58.171837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-03T16:41:00.687906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-03T16:41:10.944183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-03T16:41:11.189943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-03T16:41:11.433773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-03T16:41:11.679714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-03T16:41:03.353746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-03T16:41:03.759936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-03T16:41:04.044372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-03T16:41:04.288509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0Afghanistan2015Developing65.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
1Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
2Afghanistan2013Developing59.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
3Afghanistan2012Developing59.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
4Afghanistan2011Developing59.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
5Afghanistan2010Developing58.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
6Afghanistan2009Developing58.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
7Afghanistan2008Developing58.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
8Afghanistan2007Developing57.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
9Afghanistan2006Developing57.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1

Last rows

CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
2928Zimbabwe2009Developing50.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9
2929Zimbabwe2008Developing48.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7
2930Zimbabwe2007Developing46.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6
2931Zimbabwe2006Developing45.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5
2932Zimbabwe2005Developing44.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3
2933Zimbabwe2004Developing44.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2
2934Zimbabwe2003Developing44.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5
2935Zimbabwe2002Developing44.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0
2936Zimbabwe2001Developing45.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8
2937Zimbabwe2000Developing46.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887812222251.011.011.20.4349.8